Neuronal circuits exhibit specialization at many levels; in particular, neuronal diversity and differences in connectivity are thought to be crucial. Significant advances have been made in understanding the types of plasticity that may contribute to learning and memory in the hippocampus, but much less has been discovered about how the circuit stores and extracts information. A key aspect of all circuit function is the diverse population of inhibitory interneurons, which differ in physiological properties, dendritic morphology and axon targeting. Understanding of circuit function can be significantly enhanced by capturing the complexity inherent in neuronal diversity and connectivity in detailed computer models of the system. Here we propose to study and model specific populations on interneurons in the hippocampus identified in BAG transgenic mice generated by the NINDS-Gensat project. We propose to record from identified neurons in these mice in order to determine their physiological properties. The recorded cells will also be stained so that dendritic morphology can be determined and quantified. Computational models will then be generated of the interneurons and pyramidal neurons to which they project for the purpose of making experimentally testable predictions concerning hippocampal circuit function. This is a collaborative project that brings together investigators, students, and postdocs to take a multidisciplinary approach to the study of hippocampal circuit function. The project has five specific aims: 1) Physiological investigation of hippocampal interneurons in BAG transgenic mice. 2) Anatomical investigation of hippocampal interneurons in BAG transgenic mice. 3) Studies of modulation of interneurons in BAG transgenic mice. 4) Modeling hippocampal interneurons from BAG transgenic mice. 5) Developing advanced computational methods for microcircuit modeling. The proposed work has important implications for several neurological disorders, including Alzheimer's disease, epilepsy, and schizophrenia. [unreadable] [unreadable] [unreadable]